Building the Model

A Model of Excellence answers the following question: “What is the difference
between a top performer and a low performer?” This page focuses on several
approaches one can follow to come up with a model, independent of the technology
being used, and independent whether one considers the attitude, the values
or the competencies (or all of them).

1. Standard Group
In this purely statistical method one takes a group of best performing
persons for a certain function and gathers the same information for each
of them. For instance, one asks each of the respondents to fill out the
iWAM, the VSQ or COMET questionnaire. Based on the scores of this whole
test group, one computes the average score for each of the questionnaire’s
parameters as well as the standard deviation for each of the parameters.

Below, Figure 1 gives an example of such a standard group
for the 16 operating factors measured by the iWAM questionnaire:

2. Contrastive Analysis
This method is achieved by comparing example of excellence with "counter-examples,”
given by persons who do not display excellence. For iWAM and VSQ, jobEQ
has developed a statistical approach based on this principle. You take
3 or more proven high performers for a certain job function, have them
fill out the questionnaire and compare their scores with scores of at
least 3 low performers holding the same function.

The principle of contrastive analysis can be combined
with a statistical approach. It is easy to draw a chart indicating the
“standard group” for high performers and comparing that area
with the standard group of low performers. One can also use the data to
see which parameters showed a significant difference between the groups.

Figure 2 shows again the same 8 persons consisting the
model of Figure 1. In this figure, the group has been split up according
to their performance. Parameters for which the data showed a significant
difference between the means will be visually recognizable (e.g. OF1+
indicates “starting”: the top performers value “taking
initiative” to a much higher extend than the persons considered
“low performers”. OF1- indicates that top performers have
less patience, etc.) For significant parameters there will be little or
no overlap between the scores for both groups.

When one compares Figure 1 to Figure 2, one will also
notice that the zone that will be considered “the Model of Excellence”
will be much more narrow for figure 2. A contrastive analysis results
in a model with a higher degree of precision. For instance, using the
model of figure 1, we might consider a person with a score of 25% for
OF4- as being “within range”, while figure 2 indicates that
the only persons with a score of more than 19% are those belong to the
least performing respondents.

3. Engineering the Model
Sometimes a large group of persons holding the function is not available,
due to a lack of people to test. Also, sometimes the company cannot or
will not have low performers go through the modeling phase, thus making
approach 2 impossible. Finally, a job may be new, so that there are no
successful examples of persons holding the function. In those cases, we
cannot build a statistical reliable model. This leaves us 2 options.

First, one can build a model based on general knowledge
about the function and the context the person will have to operate in
(the management style, company culture, etc). Secondly, one can select
some persons who have held the function or who one deems able of holding
the function and build a model inspired on their scores. And of course
both options can be combined.

This approach is more limited than others, because we
only have information to situate the top performers, but no information
on the significance of the zone we consider the model of excellence. This
lack of information can be lessened with general knowledge of what are
considered good patterns for the function and of what is the standard
group for the given culture (either the country the selected persons are
working in or their company culture).

4. Modeling Competencies using COMET
The description of the 3 previous approaches always starts from
jobEQ’s iWAM & VSQ questionnaires. This makes sense for determining
attitude and values. Similarly, you can apply these approaches on the COMET/EQ
questionnaire, showing which emotional intelligence competencies
make a difference when it comes to successful leadership, for instance.

However, given the wide array of competences one can distinguish,
a questionnaire such as
COMET/EQ may indicate necessary competencies, but cannot guarantee to
help to generate the full list of needed competencies. Modeling can learn whether
COMET/EQ competencies would be necessary for the job, but it doesn’t
help us build a sufficient list of competencies.

That’s why jobEQ defined COMET as a methodology for which we also make a 360° test tool available.
The COMET/EQ questionnaire on the jobEQ website is actually just an application,
showing the use of an assessment tool based on this COMET methodology,
and the demo-version only allows to use it in the context of self-assessment.
However,the core of the COMET method is building a competence grid and a competence dictionary for a
specific job function, based on an analysis of the competencies present in top
performers holding that function. By preference this analysis also includes
a contrastive element, where we compare these competencies with competencies
found in lower performers.

A custom questionnaire is then developed based on the
competence dictionary. This questionnaires tests to what degree the person
tested applies the behaviors described in the competence dictionary as
defining elements of the competencies. Ideally this questionnaire is tested
statistically on top performers and low performers to see whether it helps
to predict job performance, before it is administered to third persons.

Conclusion
While we recommend a custom approach for modeling competencies, the 3
standard approaches described in this paper will generally result in very
good models. Even if one designs a custom questionnaire, it is recommended
to apply the standard approaches where possible.

The approach you use will be tailored to your specific
organization. The main questions will be “What kind of information
can the company offer about its top performers?" “How precise
can we distinguish top performers from other groups?” and “How
many persons can be tested?”